This paper analyzes the features of monotonic translations, which follow the
word order of the source language, in simultaneous interpreting (SI). The word
order differences are one of the biggest challenges in SI, especially for
language pairs with significant structural differences like English and
Japanese. We analyzed the characteristics of monotonic translations using the
NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset
and found some grammatical structures that make monotonic translation difficult
in English-Japanese SI. We further investigated the features of monotonic
translations through evaluating the output from the existing speech translation
(ST) and simultaneous speech translation (simulST) models on NAIST
English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset as well
as on existing test sets. The results suggest that the existing SI-based test
set underestimates the model performance. We also found that the
monotonic-translation-based dataset would better evaluate simulST models, while
using an offline-based test set for evaluating simulST models underestimates
the model performance.

分析同传中顺译特征，在英日语言对中特别是英语和日语这种具有显著结构差异的语言对中，词序差异是同传的最大挑战之一。通过使用 NAIST 的英日顺译数据集对顺译特征进行了分析，并发现了一些在英日同传中使顺译困难的语法结构。通过评估现有语音翻译和同传语音翻译模型在 NAIST 的英日顺译数据集和现有测试集上的输出，进一步研究了顺译特征。结果表明，现有的基于同传的测试集低估了模型的性能。同时，发现基于顺译的数据集可以更好地评估同传模型，而使用离线测试集评估同传模型会低估模型的性能。

英日同传中的词语顺序：基于分块单调翻译的分析与评估

Word Order in English-Japanese Simultaneous Interpretation: Analyses and  Evaluation using Chunk-wise Monotonic Translation

We describe a cross-lingual transfer method for dependency parsing that takes
into account the problem of word order differences between source and target
languages. Our model only relies on the Bible, a considerably smaller parallel
data than the commonly used parallel data in transfer methods. We use the
concatenation of projected trees from the Bible corpus, and the gold-standard
treebanks in multiple source languages along with cross-lingual word
representations. We demonstrate that reordering the source treebanks before
training on them for a target language improves the accuracy of languages
outside the European language family. Our experiments on 68 treebanks (38
languages) in the Universal Dependencies corpus achieve a high accuracy for all
languages. Among them, our experiments on 16 treebanks of 12 non-European
languages achieve an average UAS absolute improvement of 3.3% over a
state-of-the-art method.

本文针对源语言和目标语言单词顺序的不同问题，提出了一种基于 “圣经” 语料库和跨语言词表示的依存句法跨语言迁移方法。该方法在训练前改变源语言语料的顺序，使得在欧洲语系以外的语言中达到更高的精度。在多语言数据集上，该方法在 68 个树库（38 种语言）的实验中表现良好，并在 12 种非欧洲语言的 16 个树库中实现了 3.3％的平均 UAS 绝对精度提高。